Instructions to use Hriday75/qwen2.5-3b-infectious-disease-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Hriday75/qwen2.5-3b-infectious-disease-chat with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Hriday75/qwen2.5-3b-infectious-disease-chat") - Notebooks
- Google Colab
- Kaggle
Qwen2.5-3B Infectious Disease Chat Assistant (SFT)
Model Details
Model Description
This is a highly specialized, supervised fine-tuned (SFT) LoRA adapter for the Qwen2.5-3B model. While the Phase 1 model learned dense virology, epidemiology, and pathophysiology, this Phase 2 model was specifically instruction-tuned to communicate those facts with excellent bedside manner. It is designed to act as an empathetic, conversational infectious disease expert.
- Developed by: Hriday75
- Model type: LoRA Adapter (PEFT)
- Language: English
- Finetuned from base model:
unsloth/Qwen2.5-3B-bnb-4bit(via Phase 1 Infectious Disease LoRA) - Training Stage: Phase 2 (Supervised Fine-Tuning / Instruction Tuned)
Uses
This model is intended to be used in a conversational interface. It is designed to answer patient questions about infections, explain viral transmissions, clarify lab results (like white blood cell counts or PCR tests), and maintain a reassuring, helpful tone.
Training Details
Training Data
This adapter was trained on structured conversational datasets featuring thousands of back-and-forth patient/doctor interactions. The primary dataset used was ruslanmv/ai-medical-chatbot, which was formatted into the standard ChatML structure (System, User, Assistant).
Training Procedure
Trained using unsloth and the TRL (Transformer Reinforcement Learning) SFTTrainer for high-efficiency instruction tuning.
How to Get Started with the Model
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
# 1. Load the base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit")
# 2. Attach this Infectious Disease Chat Adapter
model = PeftModel.from_pretrained(base_model, "Hriday75/qwen2.5-3b-infectious-disease-chat")
# 3. Format your chat prompt
messages = [
{"role": "system", "content": "You are a helpful, empathetic infectious disease expert."},
{"role": "user", "content": "Doctor, my latest blood panel shows an elevated white blood cell count and a high CRP. Should I be worried about an infection?"}
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
- Downloads last month
- 6
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Hriday75/qwen2.5-3b-infectious-disease-chat")